1 Introduction
The chemical compositions of different environment media, particularly the distribution of potentially toxic elements (PTEs), reflect a complex interplay between natural (geogenic) processes, including parent lithology, weathering intensity, and geochemical cycling, and a wide range of anthropogenic inputs, such as industrial discharge, mining, agriculture, urbanization, etc. However, the central challenge in contemporary geochemical research is not simply recognizing this dual influence, but the difficulty in reliably distinguish them (). Over the past few decades, geochemical background (GB) values have become a cornerstone scientific tool in environmental research for defining pollution and identifying the source of contamination (–). However, despite decades of research, the determination of GB values for PTEs remains methodological inconsistent, conceptually ambiguous, and strongly dependent on spatial scale (, , ).
In principle, GB values, which originally developed for mineral exploration, represent the natural concentration of an element in a given environmental medium, such as soil, sediments or water that is consistently present in pristine conditions and has not been influenced by localized human activities, providing a reference for distinguishing natural variability from anthropogenic sources (, , ). In contrast, geochemical baseline, which often used interchangeably with GB in the scientific literatures, has a distinct concept, which refers to the current state of element concentrations in soils at a specified location, encompassing both natural processes and diffuse anthropogenic inputs (, ). Both concepts provide a reference framework for various environmental applications, including source apportionment, anomalies detection and the quantification of contamination levels (, ). Moreover, GB values also essential for developing environmental quality guidelines, which often ignored in national regulatory standards (, ), as well as for land-use planning and resource mapping (, ). However, despite these advantages, their inconsistent definition and application across studies contribute to significant uncertainty and challenges in environmental evaluation (, , ).
The lack of conceptual clarity is emerged by methodological heterogeneity and there is no agreement over the best and most widely accepted methods for establishing GB (). A wide range of approaches, such as mean ± SD, median ± 2MAD, box-whisker diagram, iterative 2, cumulative distribution function, and Tucker Inner Fence (TIF) employed to derive GB and reference values, which obtained based on the sample statistics (–, , , , ). However, these methods often yielding significantly different results for same region (, ). Also, some of these methods have limitations in limited sample size and skewed data distribution (, ). Therefore, methodology for establishing GB is remained open for discussion (, , ). Such discrepancies are further amplified in geochemically heterogenous systems, where lithological variability, mineral deposits, weathering/pedogenic process, and land-use intensity create multiple overlapping geochemical populations (, ). As a consequence, contamination assessment of PTEs in such environmental system may lead to overestimate risks in metal-rich lithologies or underestimate of pollution in geochemically depleted regions.
Despite these limitations, the robustness and precision of methods make it valuable approaches for establishing GB values (). Advances in geochemical mapping, geostatistics, and geospatial technologies have significantly improved the resolution and interpretation of GB-derived data (, , ). The integration of high-density sampling, advanced GIS techniques and high-resolution spatial data of lithology, mineralogy, soil type, land use and land cover patterns enables more robust delineation of GB values, distinguish geochemical provinces and enhances spatial resolution for better identification of sampling points with naturally elevated concentrations versus anthropogenic signals (, , , ). More recently, integration of machine learning (ML) and artificial intelligence (AI) techniques to regional geochemical data have emerged as a powerful way forward for modeling complex and non-linear relationships in geochemical data and predicting the source and distribution of PTEs (, ). While, these techniques offer significant potential in geochemical studies, in assessing environmental risk and developing remediation strategies, their methodological application still requires critical evaluation and careful integration within a coherent conceptual framework in addressing environmental problems effectively.
Against this backdrop, there is a pressing need to move towards a more standardized, scalable and critically informed framework for GB determination and geochemical mapping. In this context, the Research Topic entitled “Advanced Geochemical Mapping and Geochemical Background/Baseline: An Environmental Perspective” published in Frontiers in Soil Science and Frontiers in Environmental Science brings together recent collections (including original research articles and review papers) aimed at addressing what is new and what lies ahead in the field. The schematic framework (Figure 1) illustrates how geochemical background serves as the central integrative components linking cutting-edge methodologies for defining geochemical background/baseline in diverse environmental or geological compartments, high-resolution multi-elemental analysis, integration of GIS and other geospatial technologies, robust geostatistical and compositional data analysis, and AI/ML models for key environmental outcomes, including source apportionments challenges, establish site-specific GB framework, contamination risk prediction, policy development, land use planning and mineral exploration and highlights the interconnected nature of geological, geomorphological and land use factors in shaping geochemical variability.
Figure 1
2 Key contributions
This Research Topic accepted eight contributions, including seven original studies and one review, which represent a diverse international collaborations across 20 institutions from 5 countries−including India, China, Brazil, United States, and Czech Republic. This underscores the global relevance of geochemical background research. Collectively, these studies demonstrated several important trends and methodological developments in the topic by advancing methodological and statistical rigor, integrating multi-disciplinary approaches, and highlighting the complexity of defining GB values across diverse environmental system.
A key theme across the contributions is the integration of GB determination with high-resolution spatial mapping and robust statistical frameworks, including principal component analysis, cluster analysis, Spearman correction analysis, and compositional data analysis. These approaches are widely used in handling large geochemical datasets, addressing the inherent complexities of multi-element geochemical datasets and improve the discrimination between geogenic signature from anthropogenic input. Some contributions demonstrate the importance of geological and mineralogical context in determining the distributions of PTEs. In this context, Amarante et al. combined geochemical mapping with lithological data and robust statistics approaches to demonstrate geochemical baseline concentrations of PTEs and explored major geochemical associations that control variability of PTEs in stream sediments in the Vazante Zinc District of Minas Gerais, Brazil. This study reveal that natural control such as ultramafic rocks are mainly controlling the enrichment of Ni, Cr and Co in this heterogeneous system, and underscores the need of considering site-specific GB linked to certain mineralogical controls for effective monitoring and regulatory planning in the region. In another study from Yangtze River Source Area, Tibetan Plateau, Li et al. studied geochemical mapping with mineralogical data and grain-scale sedimentology to explore the interaction of geochemical and physical properties in complex river system. Their findings demonstrate that physical properties like grain size should be explicitly considered while planning river flood control and engineering assessment to avoid misinterpretation of geochemical signals.
Another significant advancement highlighted in this Research Topic is the increasing application of AI and advanced ML models in geochemical prediction and risk assessment of contaminants. In this regard, Kerketta et al. studied the role of different machine learning models such as Random Forest, eXtreme gradient boosting (XGBoost), extreme learning machine (ELM), multi-layer perceptron (MLP) in predicting fluoride (F−) concentrations in shallow aquifers of Punjab based on basic water quality variables. The model identified that TDS along with Na+, Cl−, and Ca2+ as key predictors of F− risk in groundwater. This work demonstrates that, advanced ML techniques can effectively map contamination risk and aid in sustainable management of groundwater resources. Ghosh et al. integrate ML models with geochemical data to predict potassium (K) dynamics in four major Indian soils (red, alluvial, calcareous, black) in both natural and flooded conditions. RF models efficiently predict K availability and uptake, highlighting ML methods can be used as a powerful tool for optimizing fertilizer management.
Furthermore, the contributions also emphasize the link between land use and geochemical background, particularly in agricultural systems. Sahoo et al. examined accumulation of PTEs in vegetable-cultivated soils from Malwa Punjab, focusing on how crop rotation practices influence PTE accumulation. The study defined baseline values of PTEs using uncultivated soils and demonstrated the necessity of developing site-specific baseline or background values in contamination assessment rather than global/Indian reference limits and highlight agricultural activities, especially the excessive application of DAP is significantly contributing PTEs enrichment in soils. In another study, Pantuzzo et al. proposed rapid and low-cost indices (IRS1 and IRS2) to distinguish tailings from background sediment to trace mining impact in river sediments, especially affected by iron mining tailings in Brazil following the Brumadinho dam collapse. These indices demonstrate a simple process for real-time monitoring of mining impacts and rapid screening of contamination signatures in a complex riverine environment, which can be helpful in remediation purpose. In a similar manner, Saleh et al. investigated lead (Pb) speciation in paint-contaminated residential soils from Baltimore, San Antonio, and Detroit (USA), focusing on remediation strategies. The study linked geochemical speciation and baseline understanding to remediation strategy.
Finally, the inclusion of a review by Asare et al. on the plant-soil-microbe interactions in PTE dynamics extends the scope of GB research beyond abiotic control, emphasizing the role of biological process in controlling metal mobility and bioavailability. Taken together, all these studies collectively demonstrate that advancing geochemical background research requires not only improved statistical and analytical tools, but also a conceptual shift towards integrative multi-disciplinary approaches. This is fundamental for advancing our understanding of better application of GB values in diverse environmental studies.
3 Key challenges
Despite substantial advances in geochemical background research, its definition and application remain methodologically inconsistent and conceptually ambiguous, limiting its reliability in environmental assessment. A central challenge lies in defining a “natural” geochemical background, which ideally requires pristine environment condition that is completely unaffected by human activity; such conditions are increasingly rare or non-existent, and that “natural” itself is a contested construct (). Thus, the “natural background” is an idealized reference condition that must be statistically estimated or modeled than directly observed. Furthermore, there is still difficulty in defining GB levels in geochemically heterogenous systems, where wide variations in parent lithology, mineralogical compositions, local weathering conditions, soil formation processes and land-use intensities generate multiple overlapping populations in geochemical data (, ). In such contexts, it will be quite difficult to give a clear distinction between geogenic and anthropogenic contributions. Moreover, the mobility and bioavailability of PTEs are controlled by complex geochemical reactions, redox conditions, pH, organic matter content, and microbial interactions, further challenging the accurate estimation and interpretation of background values ().
A second major limitation arises from methodological constraints including lack of standardized sampling design, sampling size analytical protocols for multi-elements analysis (e.g., aqua regia extraction versus aqua regia plus HF versus lithium tetraborate fusion methods), and data treatment approaches, which lead to significant inconsistencies across studies. These differences hinder the comparability of datasets. As a result, this would be a great challenge in integrating different datasets in defining geochemical backgrounds at a national or global scale for diverse environmental media such as soils, sediments, stream waters (, , ). Also, geochemical mapping and background estimation requires spatial modeling based on sparce, heterogenous and potentially inaccurate data sets. Hence, it is vital for uncertainty quantification to provide accurate and reliable results for well-informed decision-making (). Moreover, while multivariate statistical methods and GIS-based mapping are widely used, there is a lack of standardized protocol for data handing and uncertainty quantification, leading to divergent interpretations.
Finally, a critical yet underexplored challenge is the translation of GB values into actionable environmental guidelines and policy making frameworks. Existing environmental guidelines often rely on fixed threshold values that ignore the geological context and conflict with natural background levels (), resulting in the potential misclassification of naturally elevated element concentrations as pollution or, conversely, the underestimation of anthropogenic impacts in depleted systems (). Therefore, transforming scientifically rigorous GB or baselines into actionable environmental guidelines and intervention values remains a policy and scientific translation challenge.
4 Future prospects
Addressing the above-mentioned challenges requires a shift towards integrated, standardized, site-specific and multi-disciplinary research approaches that combine geochemical mapping, robust statistics, advanced modeling, spatial mapping and emerging data-driven AI/ML technologies. Future research should focus on the development of robust framework for GB determination that explicitly include standardized protocol, geological/mineralogical context, uncertainty, and spatial variability. Establishing a standardized protocols for sampling, analytical technique, compositional data analysis, and uncertainty analysis can enhance comparability across studies and facilitate the development of coordinated regional to global geochemical database. Further, to improve the reliability of background estimation in complex system, the site-specific geological and mineralogical information is crucial to avoid misinterpretation of natural anomalies as pollution (, ).
Scaling up large-scale geochemical mapping through coordinated regional/national surveys following standard methods/protocol represent another important step towards capturing spatial variability and establishing effective GB across multiple scales. However, such efforts should follow scale dependency, as sampling density would vary significantly between local, regional and continental contexts.
The integration of ML, AI and big-data analytics with geochemical data can offer significant potential for advancing methodological and technological innovation and improve geochemical predictions and mapping. These tools can handle large and multi-dimensional geochemical datasets, reveal complex and non-linear relationships between variables and can better capture spatial and temporal patterns of element distributions (, , ). This can also contribute to expansion of global geochemical databases for benchmarking and developing real-time monitoring systems for dynamic environmental changes. For example, Random Forest and neural networks could be applied to integrate soil, geology, and land-use layers. Lastly, addressing anthropogenic pressures such as agriculture and urbanization impacting geochemical baselines via integrated geochemical and geospatial tools with AI is critical for sustainable environmental governance (, ). However, rather replacing traditional approaches, their application must be used cautiously alongside as a complementary tool within a broader methodological framework.
In parallel, the development of digital geochemical data repositories and shared platform is essential for maximizing the utility of baseline geochemical data, enable cross-regional comparisons and facilitates the integration of local studies into continental and global frameworks. Finally, there is a discrepancy between regulatory thresholds and natural geochemical background. Robust natural background values increase the accuracy of pollution assessment and can effectively guide the formulations, management, and implementation of remediation policies (). Hence, the future efforts should also focus on redefining environmental guideline and policy based on site-specific GB values, which often ignored in national environmental quality guidelines.
Thus, future environmental quality guideline should integrate background values along with absolutely clean up target rather than one limits for national/worldwide value. Bridging scientific baselines with regulatory standards through dialogue with scientists, policymakers and stakeholders will be crucial for the broader environmental utility of geochemical research.
5 Conclusions
The editorial synthesizes the contributions of eight articles that collectively presents the methodological advances in geochemical mapping and background studies. The findings highlight that site-specific GB values are essential for reliable contamination assessment, particularly in geochemically heterogenous and anthropogenically influenced environment. Furthermore, the integration of multivariate statistics with advance geochemical mapping and ML approaches a way forward to address complex contamination and source apportionment challenges. This leads to accurately identify the presence of multiple populations and distinguish anthropogenic input from natural variability in geologically heterogenous systems. However, these advancements also face persistent challenges related to lack of methodological rigor and robust scientific framework, as well as issues with analytical data quality. These limitations pose significant hurdles to the development national or global-scale GB values. Addressing these challenges require conceptual clarity, uncertainty in GB estimation, methodological standardization, the integration of geochemical data with high-resolution mapping, robust statistics and ML/AI models, interdisciplinary study. Also, stronger collaboration between scientists and policymakers is essential to ensure more reliable and decision-relevant environmental assessment,. Moreover, we also need to bridge the gap between scientific understanding and practical implementation, ensuring that insights from robust GB values can effectively inform actionable environmental guidelines and policy frameworks towards a better environmental protection, improved human health assessment, and sustainable resource management in a rapidly changing world.
Statements
Author contributions
PS: Writing – original draft, Conceptualization. GS: Conceptualization, Methodology, Writing – review & editing. EM: Writing – review & editing, Formal analysis.
Acknowledgments
PKS acknowledges the DST-SERB (Department of Science and Technology, Science and Engineering Research Board), New Delhi (Government of India) for supporting this work through a Core Research Grant (CRGR/2021/002567).
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author PS declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
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The author(s) declared that generative AI was not used in the creation of this manuscript.
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Summary
Keywords
environmental risk assessment, geochemical background/baseline, geochemical mapping, GIS and artificial intelligence, potentially toxic elements (PTEs)
Citation
Sahoo PK, Salomão GN and Marques ED (2026) Editorial: Advanced geochemical mapping and geochemical background/baseline: an environmental perspective. Front. Soil Sci. 6:1806238. doi: 10.3389/fsoil.2026.1806238
Received
07 February 2026
Revised
14 April 2026
Accepted
21 April 2026
Published
25 May 2026
Volume
6 - 2026
Edited by
Sabine Grunwald, University of Florida, United States
Reviewed by
Morufu Olalekan Raimi Mnes, Federal University, Nigeria
Updates
Copyright
© 2026 Sahoo, Salomão and Marques.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Prafulla Kumar Sahoo, prafulla.iitkgp@gmail.com
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.